A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns: Sequences of Discrete Symbols

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Author(s): T. Veena (Indian Institute of Technology Madras, India), A. D. Dileep (Indian Institute of Technology Madras, India)and C. Chandra Sekhar (Indian Institute of Technology Madras, India)
Copyright: 2013
Pages: 21
Source title: Bioinformatics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-3604-0.ch004

Keywords: Bioinformatics / Medical Information Science Reference / Medicine & Healthcare

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Abstract

Pattern analysis tasks on sequences of discrete symbols are important for pattern discovery in bioinformatics, text analysis, speech processing, and handwritten character recognition. Discrete symbols may correspond to amino acids or nucleotides in biological sequence analysis, characters in text analysis, and codebook indices in processing of speech and handwritten character data. The main issues in kernel methods based approaches to pattern analysis tasks on discrete symbol sequences are related to defining a measure of similarity between sequences of discrete symbols, and handling the varying length nature of sequences. We present a review of methods to design dynamic kernels for sequences of discrete symbols. We then present a review of approaches to classification and clustering of sequences of discrete symbols using the dynamic kernel based methods.

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A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns: Sequences of Discrete Symbols

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